Finally, to mimic the KCs receiving global inhibition from each other, we subtract the mean of all hidden nodes outputs from each intermediate output in Eq. (REF ). That computation can be viewed as a form of the layer normalization in machine learning [1]}, which helps to maintain the contributions of each feature so that the network can be unbiased, reduce the internal covariance shift, and accelerate the optimization process. Thus, our model helps to import insights from machine learning into the interpretation of the adaptive function of global inhibition in neuronal structures like the mushroom body.
